Triplet Decoupling Network for Masked Face Verification

Abstract

Face verification has been widely applied to identity authentication in many areas. However, due to the mask information embedded into the facial feature representation, existing face verification systems generally fail to identify the faces covered by masks during the COVID-19 coronavirus epidemic period. To address this issue, we propose a new triplet decoupling network (TDN) for the challenging masked face verification. Different from existing works, our proposed TDN seeks to remove the mask information included in extracted facial features by feature decoupling, such that more discriminative facial feature representations can be obtained for masked face verification. In addition, a new triplet similarity margin loss (TSM) is designed to enlarge the margin between the intra-class similarity and the inter-class similarity of faces. Experimental results show that the proposed method significantly outperforms the other state-of-the-art methods on masked face datasets, which demonstrates the effectiveness of our proposed method.